Qasim M. Zainel1, Murad B. Khorsheed2, Saad Darwish3,*, Amr A. Ahmed4
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3813-3828, 2021, DOI:10.32604/cmc.2021.014759
Abstract Convolutional Neural Networks (CNNs) models succeed in vast domains. CNNs are available in a variety of topologies and sizes. The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture. Our proposed framework to automated design is aimed at resolving this problem. The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit. In comparison to the co-authored work, our proposed framework is concerned… More >
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